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import pathlib
import os
import torch
from tops.config import LazyCall as L
if "PRETRAINED_CHECKPOINTS_PATH" in os.environ:
PRETRAINED_CHECKPOINTS_PATH = pathlib.Path(os.environ["PRETRAINED_CHECKPOINTS_PATH"])
else:
PRETRAINED_CHECKPOINTS_PATH = pathlib.Path("pretrained_checkpoints")
if "BASE_OUTPUT_DIR" in os.environ:
BASE_OUTPUT_DIR = pathlib.Path(os.environ["BASE_OUTPUT_DIR"])
else:
BASE_OUTPUT_DIR = pathlib.Path("outputs")
common = dict(
logger_backend=["wandb", "stdout", "json", "image_dumper"],
wandb_project="deep_privacy2",
output_dir=BASE_OUTPUT_DIR,
experiment_name=None, # Optional experiment name to show on wandb
)
train = dict(
batch_size=32,
seed=0,
ims_per_log=1024,
ims_per_val=int(200e3),
max_images_to_train=int(12e6),
amp=dict(
enabled=True,
scaler_D=L(torch.cuda.amp.GradScaler)(init_scale=2**16, growth_factor=4, growth_interval=100, enabled="${..enabled}"),
scaler_G=L(torch.cuda.amp.GradScaler)(init_scale=2**16, growth_factor=4, growth_interval=100, enabled="${..enabled}"),
),
fp16_ddp_accumulate=False, # All gather gradients in fp16?
broadcast_buffers=False,
bias_act_plugin_enabled=True,
grid_sample_gradfix_enabled=True,
conv2d_gradfix_enabled=False,
channels_last=False,
compile_G=dict(
enabled=False,
mode="default" # default, reduce-overhead or max-autotune
),
compile_D=dict(
enabled=False,
mode="default" # default, reduce-overhead or max-autotune
)
)
# exponential moving average
EMA = dict(rampup=0.05)
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